AIがハリケーンに強い建物の設計に新たな基準を設ける可能性(AI Could Set a New Bar for Designing Hurricane-Resistant Buildings)


2023-03-29 米国国立標準技術研究所(NIST)

A computer window depicts an overhead view of an Atlantic hurricane making landfall on the U.S. East Coast.




大西洋のハリケーンコースと特徴のシミュレーション: 機械学習と連動したアプローチ Simulation of Atlantic Hurricane Tracks and Features: A Coupled Machine Learning Approach

Rikhi Bose,Adam L. Pintar and Emil Simiu

Artificial Intelligence for the Earth Systems  Published:27 Mar 2023



The objective of this paper is to employ machine learning (ML) and deep learning (DL) techniques to obtain, from input data (storm features) available in or derived from the HURDAT2 database, models capable of simulating important hurricane properties (e.g., landfall location and wind speed) consistent with historical records. In pursuit of this objective, a trajectory model providing the storm center in terms of longitude and latitude, and intensity models providing the central pressure and maximum 1–min wind speed at 10m elevationwere created. The trajectory and intensity models are coupled and must be advanced together, six hours at a time, as the features that serve as inputs to the models at any given step depend on predictions at the previous time steps. Once a synthetic storm database is generated, properties of interest, such as the frequencies of large wind speeds may be extracted from any part of the simulation domain. The coupling of the trajectory and intensity models obviates the need for an intensity decay model inland of the coastline. Prediction results are compared to historical data, and the efficacy of the storm simulation models is evaluated at four sites: New Orleans, Miami, Cape Hatteras, and Boston.